检索结果-内蒙古大学图书馆

Advances in Modern Age Technologies for Health and Engineering science (AMATHE), International Conference on

作者： D. Karthik S. Arun Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Managing the increasing prevalence of chronic illnesses is a major worldwide health concern. It's important to understand the medical problem, its stages, types, and available treatments. The most important component in the early diagnosis of chronic diseases might enhance the diagnostic procedure more successfully. Medical professionals can diagnose a process with the help of numerous artificial intelligence tools. However, ambiguity, irrelevance, and inconsistency in the disease dataset have an impact on the classification accuracy. Conventional machine learning techniques are inadequate for managing scenarios of uncertainty that arise in datasets related to chronic illnesses. To anticipate chronic diseases, each characteristic in the suggested work is represented with a hesitation index. In order to compute the hesitation degree as a more informative procedure in determining the association among features, the belongingness and non-belongingness aspects of the intuitionistic fuzzy concept are induced in the prediction process. This work effectively manages the large chronic illness dataset by developing an intuitionistic fuzzy hesitation index-based similarity measure to weed out unimportant and strongly associated characteristics and enhance the classification model. Utilizing the scored feature information, the deep neural network modifies its hyper parameter to address the overfitting issues that frequently arise in DNNs. The performance of the empowered intuitionistic fuzzy-based deep neural network (EIF-DNN) is analyzed using five distinct chronic conditions, including hypertension, diabetes, hepatitis, cancer, and stroke. Comparing the proposed EIF-DNN against the current versions of multi-layered networks, the simulation results show that it produces promising results in predicting chronic diseases at an early stage.

关键词： Training Uncertainty Accuracy Artificial neural networks Stroke (medical condition) Prediction algorithms Indexes

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis of Different CNN Architectures for Malware Classification 4

A Comparative Analysis of Different CNN Architectures for Ma...

引用

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mahato, Anisha Majumdar, Rana Ghosh, Swarup Kr Sister Nivedita University Department of Computer Science Engineering Kolkata India Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9798350352931

The concept of digital privacy remains a contested and often misunderstood topic in the modern, internet-dominated era. Despite numerous advancements in cybersecurity, the pervasive threats to data security continue to erode trust in digital systems. Malware, a term encompassing a wide range of malicious software designed to disrupt operations, compromise systems, or steal sensitive information, represents a major challenge in this regard. Its widespread presence has led to the perception that digital privacy is increasingly unattainable, casting a shadow over cyberspace and intensifying the urgency for robust countermeasures. This study delves into the critical task of malware classification, leveraging the Malimg dataset, a benchmark resource widely used for malware research. To tackle this problem, four cutting-edge Convolutional Neural Network (CNN) architectures-LeNet-5, AlexNet, VGG-16 and ResNet-are employed to train and test the dataset, with the objective of identifying the architecture best suited for this classification task. The models are rigorously evaluated using four essential performance metrics: Accuracy, Precision, Recall, and F1-score, which collectively provide a comprehensive measure of their effectiveness. Among these architectures, ResNet emerges as the most effective, achieving a stellar accuracy of 99.78% and excelling across all other evaluation parameters. This highlights ResNet's superior capability in accurately identifying and classifying malware, thereby offering a promising solution to mitigate the growing threat posed by malicious software in cyberspace. © 2024 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach for Credit Card Fraud Detection using Gated Recurrent Unit (GRU) Networks

A Novel Approach for Credit Card Fraud Detection using Gated...

引用

International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： V. Suganthi J. Jebathangam Department of Computer Science Vels Institute of Science Technology & Advanced Studies(VISTAS) Chennai India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Mobile payment systems have become increasingly popular with the rise of smartphones, but this has also attracted fraudsters. As fraud rates continue to rise, researchers have turned to machine learning methods to detect and analyze fraudulent transactions effectively. The purpose of this study is to propose the use of the Gated Recurrent Unit (GRU) is an effective method for analyzing and detecting patterns in sequential data, particularly for applications such as fraud detection in mobile payment systems. The GRU is chosen for its ability to capture temporal dependencies in data while being computationally efficient compared to other recurrent neural network variants like LSTM. This study will explore the implementation of GRU, evaluate its performance, and compare it with existing algorithms to highlight its advantages in real-time fraud detection scenarios. The proposed GRU method provides an efficient and effective approach for detecting fraud in credit card transactions by leveraging its ability to process and learn from sequential data. The use of GRUs can improve detection accuracy and efficiency, contributing to more robust fraud detection systems, and ultimately helping protect consumers and financial institutions from financial losses.

关键词： Accuracy Recurrent neural networks Online banking Machine learning Logic gates Credit cards Real-time systems Fraud Smart phones Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Developing an advanced Tool for Transforming Spreadsheet Data into Interactive Dashboards with Predictive Insights 2

Developing an Advanced Tool for Transforming Spreadsheet Dat...

引用

2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Bir, Varnika Kumar, Tapas Tanwar, Poonam Kumar, Pradeep Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies Faridabad India School of Engineering & Technology Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9798331541217

In today's data-driven world, organizations face the challenge of managing vast datasets. This work centres on developing an advanced tool to streamline the conversion of spreadsheet data into interactive dashboards with predictive insights. Leveraging cutting-edge technologies, the tool enables seamless data visualization and analytics, empowering users to extract meaningful insights. With integrated predictive modelling, it enhances decision-making, making it invaluable for professionals seeking efficient data-driven solutions. The tool not only visualizes data dynamically but also predicts future trends through sophisticated algorithms and machine learning. By uncovering hidden patterns, it deepens users' understanding of data dynamics. Additionally, this research meets the growing demand for intuitive, user-friendly data analytics interfaces. The tool's design ensures accessibility for users with varying technical skills, while interactive dashboards boost engagement and allow real-time exploration of complex datasets. © 2024 IEEE.

关键词： Data Analytics

来源：评论

学校读者我要写书评

暂无评论

LSTM-MI: Revolutionizing Intrusion Detection Through Adaptive Learning and Mutual Information Analysis

LSTM-MI: Revolutionizing Intrusion Detection Through Adaptiv...

引用

Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： J. Archana A.S. Aneetha Department of Computer science Vels Institute of Science Technologies and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

This study introduces two innovative approaches to augment intrusion detection capabilities. The first method employs Principal Component Analysis (PCA) for dimensionality reduction, streamlining the dataset's complexity while retaining crucial features. The second method delves into feature selection using Mutual Information (MI), identifying and preserving the most informative attributes crucial for intrusion detection. These selected features are then incorporated into LSTM-based models and it evaluates the efficacy of both PCA and MI alongside LSTM, providing a comparative analysis of their impact on intrusion detection performance. Notably, the LSTM-PCA model exhibits an accuracy of 97.73%, while the LSTM-MI model achieves a higher accuracy of 98.21 %. These results underscore the significance of integrating PCA and MI in conjunction with LSTM, offering nuanced insights into their individual contributions to accurate intrusion detection. Through extensive experimentation on benchmark datasets, the proposed methodologies aim to advance the development of robust intrusion detection systems (IDS).

关键词： Dimensionality reduction Circuits Intrusion detection Benchmark testing Feature extraction Complexity theory Distributed computing

来源：评论

学校读者我要写书评

暂无评论

Multi-criteria rating conversion without relation loss for recommender systems

引用

International Journal of computers and Applications 2022年第2期44卷 130-138页

作者： Sitkrongwong, Padipat Maneeroj, Saranya Takasu, Atsuhiro Department of Informatics School of Multidisciplinary Sciences The Graduate University for Advanced Studies SOKENDAI Tokyo Japan Advanced Virtual and Intelligent Computing Center Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand National Institute of Informatics Tokyo Japan

Traditional recommender systems let users provide a single rating indicating their overall preferences toward items. Beside overall rating, multi-criteria recommender systems let users rate on multiple aspects of items with multi-criteria ratings. Most methods in recommender systems are based on collaborative filtering, which makes recommendations by exploiting ratings from neighbor users. However, most of them ignore the fact that rating behaviors of users vary due to their personal preference biases. Thus, exploiting ratings from neighbors directly might result in a poor recommendation. To solve this, the rating conversion techniques have been applied in some single criterion recommendations. For multi-criteria ratings, converting each criterion rating independently might result in loss of relation among criteria ratings. We propose a novel method that simultaneously converts all criteria ratings between users to maintain their implicit relations. The multi-criteria ratings are first normalized by variances of users and principle component analysis is applied to extract user preference patterns. Such patterns are then used for multi-criteria rating conversion. The experiment results show that our method outperforms both current single and multi-criteria rating conversion techniques with RMSEs of 1.1082 and 3.5574 on TripAdvisor and Yahoo datasets respectively, while maintaining considerably high level of prediction coverages. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Automated Insect Detection and Classification Using Pelican Optimization Algorithm with Deep Learning on Internet of Enabled Agricultural Sector

引用

SN computer science 2024年第5期5卷 576页

作者： Assiri, Mohammed Elhameed, Elmouez Samir Abd Kumar, Arun Singla, Chinu Department of Computer Science College of Sciences and Humanities- Aflaj Prince Sattam bin Abdulaziz University Aflaj 16273 Saudi Arabia Department of Computer Science College of Post-Graduated Studies Sudan University of Science and Technology Khartoum Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Recently, the combination of Deep Learning (DL) methods within the Internet of Things (IoTs) has developed in the agricultural field, especially in the domain of pest management. This study considers the implementation and development of an innovative method for Insect Detection and Classification using DL within the environment of the IoTs in agriculture. The developed system leverages advanced DL approaches for analyzing images captured by IoT-enabled devices, enabling real-time identification and categorization of insect pests. By continuously incorporating these technologies, this research goals to increase the efficiency and precision of pest monitoring, finally providing to sustainable agricultural technologies and increased crop yield. This study presents an Automated Insect Detection and Classification using Pelican Optimization Algorithm with Deep Learning (AIDC-POADL) technique on Internet of Enabled Agricultural Sector. The main aim of the AIDC-POADL system is to detect and categorize various kinds of insects exist in the agricultural field. In the primary stage, the AIDC-POADL technique involves DenseNet-121 model to learn complex features in the input images. Besides, the hyperparameter selection of the DenseNet-121 model developed by the POA. At last, multilayer perceptron (MLP) model can be applied to discriminate the insects into various classes. To validate the improved performance of the AIDC-POADL algorithm, a series of simulations were involved. The experimental outcomes stated that the AIDC-POADL technique offers enhanced recognition results over other approaches. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Agricultural region Deep learning Insect detection Internet of things Pelican optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Node Mobility and Encounter Rate Metrics to Enhance Stability in MANET 2

Node Mobility and Encounter Rate Metrics to Enhance Stabilit...

引用

2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Rajaram, Gnanajeyaraman Archana Vishveswari, R.S. Deepa, R. Packialatha, A. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Applied Machine Learning Tamil Nadu Chennai India Varuvan Vadivelan Institute of Technology Department of Computer Science and Engineering Tamil Nadu Dharmapuri India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350325799

Mobile Ad hoc Networks (MANETs) have been broadly functional in a wide variety of scenarios, for example, in disaster recovery, health care, video conferencing, and battlefield transmissions. MANET is an independent and peer-to-peer network which builds a chain of nodes to enable data transmission from sender to receiver. Hop count is measured as the most significant metric in MANET. Though several metrics have been introduced as the replacement for the hop count metric, there is a need for outperformance in mobile scenarios. The node mobility will affect the link stability, and it creates congestion. To solve these issues, a Node Mobility and Encounter Rate metrics (NMER) to enhance the stability of the network. It selects the route based on the node mobility and Encounter Rate (ER) metrics. The congestion ingredient measures the congestion level in the MANET. The objective of this route has the least cost for forward data packets and minimizes congestion. The NMER approach simulation results demonstrate that the NMER approach minimizes the packet loss ratio and reduces the network delay. © 2023 IEEE.

关键词： Mobile ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Diverse Hazy Image Synthesis via Coloring Network

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3703-3713页

作者： Zhang, Shengdong Zhang, Xiaoqin Wan, Shaohua Ren, Wenqi Zhao, Liping Zhao, Li Shen, Linlin Shaoxing University Department of Computer Science and Engineering Shaoxing312000 China Shenzhen United Aircraft Technology Company Ltd. Guangdong Shenzhen518129 China Wenzhou University College of Computer and Artificial Intelligence Wenzhou325035 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China Sun Yat-sen University School of Cyber Science and Technology Shenzhen518110 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Convolutional neural network (CNN)-based dehazing methods have achieved great success in single image dehazing. However, the absence of real-world haze image datasets hinders the deep development of single image dehazing. To address this issue, we propose a diverse hazy image synthesis method based on generative adversarial network (GAN) and matting. Specially, we train a GAN-based model that can transform a gray image into a hazy image. To boost the diversity of hazy images, we propose to simulate hazy images via image matting, which can fuse a real haze image with another image containing diverse objects. To evaluate the performance of dehazing methods, we propose two novel metrics: part-based peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM). Extensive experiments are conducted to show the effectiveness of the proposed model, dataset, and criteria. © 2020 IEEE.

关键词： Three dimensional displays

来源：评论

学校读者我要写书评

暂无评论

Experimental Evaluation of Remote Sensing–Based Climate Change Prediction Using Enhanced Deep Learning Strategy

引用

Remote Sensing in Earth Systems sciences 2024年第4期7卷 642-656页

作者： Madhavi, Macharapu Kolikipogu, Ramakrishna Prabakar, S. Banerjee, Sudipta Maguluri, Lakshmana Phaneendra Raj, G. Bhupal Balaram, Allam Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India School of Commerce and Management Studies Dayananda Sagar University Bangalore India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune Campus Maharashtra Pune India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram Guntur India School of Agriculture SR University Warangal India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Climate change is one of the most pressing global challenges of our time, with far-reaching impacts on ecosystems, economies, and human societies. Accurate prediction of climate change patterns is crucial for developing effective mitigation and adaptation strategies. Remote sensing data, with its ability to provide comprehensive and continuous observations of the Earth’s surface, plays a vital role in monitoring and predicting these changes. However, the complexity and high dimensionality of remote sensing data present significant challenges for traditional predictive models. In this study, we present an Enhanced Deep Learning Strategy for climate change prediction using remote sensing data, integrating a Cascaded Inception-LGBM model. The proposed model combines the feature extraction capabilities of the Inception module with the predictive power of the Light Gradient Boosting Machine (LGBM). The methodology was evaluated on various climate variables, including temperature, precipitation, and CO₂ levels, achieving an accuracy of 97.22%. Comparative analysis with state-of-the-art models demonstrated the superior performance of our approach, particularly in terms of RMSE, MAE, and R² metrics. Robustness tests further confirmed the model’s generalization capabilities under different data conditions. This study underscores the potential of advanced deep learning techniques in enhancing climate change prediction accuracy and offers insights into the key drivers of climate variability. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate change prediction Deep learning Environmental monitoring Feature extraction Inception module Light Gradient Boosting Machine Model accuracy NDVI Remote sensing Spectral indices

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：